Descriptor computation:
The pipeline of computing gVLAD descriptor is characterized in Figure REF . First, all images are resized to \(1024\times 768\) . We find that when using the original resolution of Holidays images, the performance is inferior to the down-sampled images. We can also benefit from the speed when using smaller images. In Oxford and Paris datasets, bounding boxes are provided for queries. We only extract descriptors inside bounding boxes. We use the SIFT and SURF implementations in OpenCVhttp://opencv.org/ to detect keypoints and extract descriptors. Each SIFT descriptor has 128 dimensions and each SURF descriptor has 64 dimensions. We find that VLAD based features have better performance using SURF keypoints and descriptors [1]} than SIFT keypoints and descriptors [2]}. In general, we observed about 10% improvement using SURF as compared to SIFT. More details about the performance difference can be seen by comparing results in Table REF  and Table REF .
